Simple Road Side Detection and Tracking System Project using Machine Learning

2025-04-03

This project outlines a project focusing on the development of a simple road detection and tracking system using automated learning techniques. The main objective is to promote road safety and navigation by accurately identifying side objects on roads such as markings, barriers and infantry.

Introduction

The roadside detection and tracking system promotes computer vision and machine learning algorithms to analyse video feeds from vehicle-based cameras. By processing these feeds, the system can detect and track the various elements of the road in real time and provide crucial information to drivers and independent systems.

Methodology

• Data collection: Gather a diverse dataset of roadside images and videos under different lighting and weather conditions.
  
  
• Prefabricated: Clean and annotate the dataset, ensuring that all objects of interest are labeled correctly.
  
  
• Model selection: Selection of appropriate models for automatic learning, such as revolutionary neural networks, for objects detection functions.
  
  
• Training: Training of the model using the annotated set of data and maximizing accuracy and speed.
  
  
• Testing and evaluation: Evaluation of model performance on a separate test set to ensure reliability.
  
  
• Implementation: Integrating the training module into a real-time system that applies video input and outputs detected for objects with tracking capabilities.

Conclusion

This project aims to create a robust roadside detection and tracking system that can significantly improve road safety and assist in the development of autonomous driving technologies. Using automatic learning, we can enhance the accuracy and efficiency of roadside monitoring.